Modifier Genes as Therapeutics: The Nuclear Hormone Receptor Rev Erb Alpha (Nr1d1) Rescues Nr2e3 Associated Retinal Disease

Nuclear hormone receptors play a major role in many important biological processes. Most nuclear hormone receptors are ubiquitously expressed and regulate processes such as metabolism, circadian function, and development. They function in these processes to maintain homeostasis through modulation of transcriptional gene networks. In this study we evaluate the effectiveness of a nuclear hormone receptor gene to modulate retinal degeneration and restore the integrity of the retina. Currently, there are no effective treatment options for retinal degenerative diseases leading to progressive and irreversible blindness. In this study we demonstrate that the nuclear hormone receptor gene Nr1d1 (Rev-Erba) rescues Nr2e3- associated retinal degeneration in the rd7 mouse, which lacks a functional Nr2e3 gene. Mutations in human NR2E3 are associated with several retinal degenerations including enhanced S cone syndrome and retinitis pigmentosa. The rd7 mouse, lacking Nr2e3, exhibits an increase in S cones and slow, progressive retinal degeneration. A traditional genetic mapping approach previously identified candidate modifier loci. Here, we demonstrate that in vivo delivery of the candidate modifier gene, Nr1d1 rescues Nr2e3 associated retinal degeneration. We observed clinical, histological, functional, and molecular restoration of the rd7 retina. Furthermore, we demonstrate that the mechanism of rescue at the molecular and functional level is through the re-regulation of key genes within the Nr2e3-directed transcriptional network. Together, these findings reveal the potency of nuclear receptors as modulators of disease and specifically of NR1D1 as a novel therapeutic for retinal degenerations

Federated learning enables big data for rare cancer boundary detection

Although machine learning (ML) has shown promise in numerous domains, there are concerns about generalizability to out-of-sample data. This is currently addressed by centrally sharing ample, and importantly diverse, data from multiple sites. However, such centralization is challenging to scale (or even not feasible) due to various limitations. Federated ML (FL) provides an alternative to train accurate and generalizable ML models, by only sharing numerical model updates. Here we present findings from the largest FL study to-date, involving data from 71 healthcare institutions across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, utilizing the largest dataset of such patients ever used in the literature (25,256 MRI scans from 6,314 patients). We demonstrate a 33% improvement over a publicly trained model to delineate the surgically targetable tumor, and 23% improvement over the tumor's entire extent. We anticipate our study to: 1) enable more studies in healthcare informed by large and diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further quantitative analyses for glioblastoma via performance optimization of our consensus model for eventual public release, and 3) demonstrate the effectiveness of FL at such scale and task complexity as a paradigm shift for multi-site collaborations, alleviating the need for data sharing

Hepatitis C virus infection in haemodialysis and kidney transplant patients.

Chronic infection with hepatitis C virus (HCV) is an important global health problem. The prevalence of HCV is significantly higher in haemodialysis and kidney transplant patients, as compared to the general population. In spite of the relatively milder liver disease activity reported in HCV-infected haemodialysis patients, HCV infection adversely affects survival. Likewise, HCV has a detrimental effect on both patient and graft survival after kidney transplantation. However, patient survival is significantly better with kidney transplantation compared to remaining on dialysis; therefore, HCV infection alone should not be a contraindication to transplantation. Combination antiviral therapy with pegylated interferon-alpha and low-dose ribavirin is currently evolving in haemodialysis patients. Interferon-alpha (standard/pegylated) is relatively contraindicated after kidney transplantation because of an increased risk of allograft rejection. Therefore, antiviral treatment of transplant candidates while on dialysis remains the best option and may avoid the risk of HCV-associated liver and renal disease after transplantation. Large multi-centre clinical trials are required in HCV-infected haemodialysis and kidney transplant patients in order to define optimal therapeutic strategies before and after transplantation

Overlapping pathways to transplant glomerulopathy: chronic humoral rejection, hepatitis C infection, and thrombotic microangiopathy.

Transplant glomerulopathy (TG) has received much attention in recent years as a symptom of chronic humoral rejection; however, many cases lack C4d deposition and/or circulating donor-specific antibodies (DSAs). To determine the contribution of other causes, we studied 209 consecutive renal allograft indication biopsies for chronic allograft dysfunction, of which 25 met the pathological criteria of TG. Three partially overlapping etiologies accounted for 21 (84%) cases: C4d-positive (48%), hepatitis C-positive (36%), and thrombotic microangiopathy (TMA)-positive (32%) TG. The majority of patients with confirmed TMA were also hepatitis C positive, and the majority of hepatitis C-positive patients had TMA. DSAs were significantly associated with C4d-positive but not with hepatitis C-positive TG. The prevalence of hepatitis C was significantly higher in the TG group than in 29 control patients. Within the TG cohort, those who were hepatitis C-positive developed allograft failure significantly earlier than hepatitis C-negative patients. Thus, TG is not a specific diagnosis but a pattern of pathological injury involving three major overlapping pathways. It is important to distinguish these mechanisms, as they may have different prognostic and therapeutic implications

Two-Step U-Nets for Brain Tumor Segmentation and Random Forest with Radiomics for Survival Time Prediction

In this paper, a two-step convolutional neural network (CNN) for brain tumor segmentation in brain MR images with a random forest regressor for survival prediction of high-grade glioma subjects are proposed. The two-step CNN consists of three 2D U-nets for utilizing global information on axial, coronal, and sagittal axes, and a 3D U-net that uses local information in 3D patches. In our two-step setup, an initial segmentation probability map is first obtained using the ensemble 2D U-nets; second, a 3D U-net takes as input both the MR image and initial segmentation map to generate the final segmentation. Following segmentation, radiomics features from T1-weighted, T2-weighted, contrast enhanced T1-weighted, and T2-FLAIR images are extracted with the segmentation results as a prior. Lastly, a random forest regressor is used for survival time prediction. Moreover, only a small number of features selected by the random forest regressor are used to avoid overfitting. We evaluated the proposed methods on the BraTS 2019 challenge dataset. For the segmentation task, we obtained average dice scores of 0.74, 0.85 and 0.80 for enhanced tumor core, whole tumor, and tumor core, respectively. In the survival prediction task, an average accuracy of 50.5% was obtained showing the effectiveness of the proposed methods. © Springer Nature Switzerland AG 2020

A large open access dataset of brain metastasis 3D segmentations on MRI with clinical and imaging information

Abstract Resection and whole brain radiotherapy (WBRT) are standard treatments for brain metastases (BM) but are associated with cognitive side effects. Stereotactic radiosurgery (SRS) uses a targeted approach with less side effects than WBRT. SRS requires precise identification and delineation of BM. While artificial intelligence (AI) algorithms have been developed for this, their clinical adoption is limited due to poor model performance in the clinical setting. The limitations of algorithms are often due to the quality of datasets used for training the AI network. The purpose of this study was to create a large, heterogenous, annotated BM dataset for training and validation of AI models. We present a BM dataset of 200 patients with pretreatment T1, T1 post-contrast, T2, and FLAIR MR images. The dataset includes contrast-enhancing and necrotic 3D segmentations on T1 post-contrast and peritumoral edema 3D segmentations on FLAIR. Our dataset contains 975 contrast-enhancing lesions, many of which are sub centimeter, along with clinical and imaging information. We used a streamlined approach to database-building through a PACS-integrated segmentation workflow

Mitochondrial DNA copy number is associated with mortality and infections in a large cohort of patients with chronic kidney disease

Damage of mitochondrial DNA (mtDNA) with reduction in copy number has been proposed as a biomarker for mitochondrial dysfunction and oxidative stress. Chronic kidney disease (CKD) is associated with increased mortality and risk of cardiovascular disease, but the underlying mechanisms remain incompletely understood. Here we investigated the prognostic role of mtDNA copy number for cause-specific mortality in 4812 patients from the German Chronic Kidney Disease study, an ongoing prospective observational national cohort study of patients with CKD stage G3 and A1-3 or G1-2 with overt proteinuria (A3) at enrollment. MtDNA was quantified in whole blood using a plasmid-normalized PCR-based assay. At baseline, 1235 patients had prevalent cardiovascular disease. These patients had a significantly lower mtDNA copy number than patients without cardiovascular disease (fully-adjusted model: odds ratio 1.03, 95% confidence interval [CI] 1.01-1.05 per 10 mtDNA copies decrease). After four years of follow-up, we observed a significant inverse association between mtDNA copy number and all-cause mortality, adjusted for kidney function and cardiovascular disease risk factors (hazard ratio 1.37, 95% CI 1.09-1.73 for quartile 1 compared to quartiles 2-4). When grouped by causes of death, estimates pointed in the same direction for all causes but in a fully-adjusted model decreased copy numbers were significantly lower only in infection-related death (hazard ratio 1.82, 95% CI 1.08-3.08). A similar association was observed for hospitalizations due to infections in 644 patients (hazard ratio 1.19, 95% CI 1.00-1.42 in the fully-adjusted model). Thus, our data support a role of mitochondrial dysfunction in increased cardiovascular disease and mortality risks as well as susceptibility to infections in patients with CKD

Disease burden and risk profile in referred patients with moderate chronic kidney disease: composition of the German Chronic Kidney Disease (GCKD) cohort

Background A main challenge for targeting chronic kidney disease (CKD) is the heterogeneity of its causes, co-morbidities and outcomes. Patients under nephrological care represent an important reference population, but knowledge about their characteristics is limited. Methods We enrolled 5217 carefully phenotyped patients with moderate CKD [estimated glomerular filtration rate (eGFR) 30–60 mL/min per 1.73 m2 or overt proteinuria at higher eGFR] under routine care of nephrologists into the German Chronic Kidney Disease (GCKD) study, thereby establishing the currently worldwide largest CKD cohort. Results The cohort has 60% men, a mean age (±SD) of 60 ± 12 years, a mean eGFR of 47 ± 17 mL/min per 1.73 m2 and a median (IQR) urinary albumin/creatinine ratio of 51 (9–392) mg/g. Assessment of causes of CKD revealed a high degree of uncertainty, with the leading cause unknown in 20% and frequent suspicion of multifactorial pathogenesis. Thirty-five per cent of patients had diabetes, but only 15% were considered to have diabetic nephropathy. Cardiovascular disease prevalence was high (32%, excluding hypertension); prevalent risk factors included smoking (59% current or former smokers) and obesity (43% with BMI >30). Despite widespread use of anti-hypertensive medication, only 52% of the cohort had an office blood pressure <140/90 mmHg. Family histories for cardiovascular events (39%) and renal disease (28%) suggest familial aggregation. Conclusions Patients with moderate CKD under specialist care have a high disease burden. Improved diagnostic accuracy, rigorous management of risk factors and unravelling of the genetic predisposition may represent strategies for improving prognosis